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  <div class="section" id="nlp-architect-by-intel-ai-lab">
<h1>NLP Architect by Intel® AI Lab<a class="headerlink" href="#nlp-architect-by-intel-ai-lab" title="Permalink to this headline">¶</a></h1>
<p>NLP Architect is an open source Python library for exploring state-of-the-art deep learning topologies and techniques for optimizing Natural Language Processing and Natural Language Understanding neural network.</p>
<p>The library includes our past and ongoing NLP research and development efforts as part of Intel AI Lab.</p>
<p>NLP Architect can be downloaded from Github: <a class="reference external" href="https://github.com/NervanaSystems/nlp-architect">https://github.com/NervanaSystems/nlp-architect</a></p>
<div class="section" id="overview">
<h2>Overview<a class="headerlink" href="#overview" title="Permalink to this headline">¶</a></h2>
<p>NLP Architect is designed to be flexible for adding new models, neural network components, data handling methods and for easy training and running models.</p>
<p>Features:</p>
<ul class="simple">
<li>Core NLP models used in many NLP tasks and useful in many NLP applications</li>
<li>Novel NLU models showcasing novel <em>topologies</em> and <em>techniques</em></li>
<li><strong>Optimized NLP/NLU models</strong> showcasing different optimization algorithms on neural NLP/NLU models</li>
<li>Model-oriented design:<ul>
<li>Train and run models from command-line.</li>
<li>API for using models for inference in python.</li>
<li>Procedures to define custom processes for training, inference or anything related to processing.</li>
<li>CLI sub-system for running procedures</li>
</ul>
</li>
<li>Based on the following Deep Learning frameworks:<ul>
<li>TensorFlow</li>
<li>PyTorch</li>
<li>Intel-Optimized TensorFlow with MKL-DNN</li>
<li>Dynet</li>
</ul>
</li>
<li>Essential utilities for working with NLP models - Text/String pre-processing, IO, data-manipulation, metrics, embeddings.</li>
</ul>
</div>
<div class="section" id="library-design-philosophy">
<h2>Library design philosophy<a class="headerlink" href="#library-design-philosophy" title="Permalink to this headline">¶</a></h2>
<p>NLP Architect is a model-oriented library designed to showcase novel and different neural network optimizations. The library contains NLP/NLU related models per task, different neural network topologies (which are used in models), procedures for simplifying workflows in the library, pre-defined data processors and dataset loaders and misc utilities. The library is designed to be a tool for model development: data pre-process, build model, train, validate, infer, save or load a model.</p>
<p>The main design guidelines are:</p>
<ul class="simple">
<li>Deep Learning framework agnostic</li>
<li>NLP/NLU models per task</li>
<li>Different topologies (moduels) implementations that can be used with models</li>
<li>Showcase End-to-End applications (Solutions) utilizing one or more NLP Architect model</li>
<li>Generic dataset loaders, textual data processing utilities, and miscellaneous utilities that support NLP model development (loaders, text processors, io, metrics, etc.)</li>
<li><code class="docutils literal notranslate"><span class="pre">Procedures</span></code> for defining processes for training, inference, optimization or any kind of elaborate script.</li>
<li>Pythonic API for using models for inference</li>
<li>Extensive model documentation and tutorials</li>
</ul>
</div>
<div class="section" id="disclaimer">
<h2>Disclaimer<a class="headerlink" href="#disclaimer" title="Permalink to this headline">¶</a></h2>
<p>NLP Architect is an active space of research and development; Throughout future releases new models, solutions, topologies and framework additions and changes will be made. We aim to make sure all models run with Python 3.6+. We encourage researchers and developers to contribute their work into the library.</p>
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